1. Field
The disclosure relates to object classification and detection, and in particular to the use of an adaptive boosting detection method.
2. Description of the Related Art
Recent developments in digital imagery, digital video and the increase in capacity of data storage have produced many types of automatic object recognition and object identification systems and methods. Improvements in the precision of digital cameras and other image capture systems have provided unprecedented amounts of data to be analyzed and used by various analysis methods. Improvements in processing speeds have allowed for increased performance and more detailed data analysis, but efficient analysis is still important in order to reduce required time and improve power savings.
One form of detailed data analysis that the improvements in processing speeds has allowed is object classification and detection. Although, the human brain is naturally wired to perform this function automatically, it is a computationally intensive task for a computer. Object classification is the act of classifying a data sample into one or more object classes. Thus, a classifier receives a data sample and provides additional information about that sample, particularly, whether or not the sample is representative of a particular object. The data sample may comprise a data measurement such as temperature, pressure, or attendance at a sports stadium. The data sample may also be a data vector combining a number of data measurements. The data sample may also be a sound clip, a digital image, or other representation of perceptual media. For example, a data sample comprising a sound clip of music may classify the sample as belonging to a “Classical” object class, a “Rock/Pop” object class, or an “Other” object class. Classifying the sample as a “Classical” object indicates that the sound clip is representative of other “Classical” objects, which would be, e.g., other sound clips of classical music. One could thus infer that the data sample is a sound clip of classical music, or at least shares a number of characteristics of classical music, based on the computer-generated classification into the “Classical” object class.
One type of computer-implemented module that finds use in this classification process is a binary classifier. A binary classifier receives, as an input, a data sample and outputs an indication of whether the sample belongs to an object class or does not belong to an object class. Other classifiers receive a data sample and output an indication of which object class, if any, the sample belongs to. For example, if a set of images is provided which contain various commonly recognized objects (such as a balls, vehicles, umbrellas, cows, people, etc.), these images can be analyzed by an appropriately programmed computer to develop a classifier for automatic classification of objects contained in new images to be analyzed. Simple classifiers that have been created based on a single image feature of an object tend to have poor categorization performance, and thus fall into the category of “weak classifiers.” Such performance can be improved by the use of “boosting” methods, which combine a number of weak classifiers to form a “strong classifier,” which classifies objects more accurately.
While boosting is not algorithmically constrained, most boosting algorithms follow a template. Typically boosting occurs in iterations, by incrementally adding weak classifiers to form a final strong classifier. At each iteration, a weak classifier learns the training data with respect to a distribution. The weak classifier is then added to the final strong classifier. This is typically done by weighting the weak classifier in some manner, which is typically related to the weak classifier's accuracy. After the weak classifier is added to the final strong classifier, the data is reweighted: examples that are misclassified gain weight and examples that are classified correctly lose weight. Thus, future weak classifiers will focus more on the examples that previous weak learners misclassified.
One such boosting method is an adaptive boosting detection method which is the method most used to train a cascade of strong classifiers using a training set of object images so that the strong classifier is effective in detecting the object in new images. Traditional use of the adaptive boosting method requires intensive computation and thus, a large amount of time, to train the system and use it for object detection. Improvements to the detection speed may enable real-time applications.